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Most of these jokes were posted to Usenet news groups. Peo­ple who read such things col­lected them and put them on their web sites. I have shame­lessly bor­rowed them, edited them and posted them here for the light relief of other statisticians.

The Biol­o­gist, the Sta­tis­ti­cian, the Math­e­mati­cian, and the Com­puter Scientist

A biol­o­gist, a sta­tis­ti­cian, a math­e­mati­cian, and a com­puter sci­en­tist are on a photo-​​safari in Africa. They drive out into the savan­nah in their jeep, stop, and scour the hori­zon with their binoculars.

The biol­o­gist: “Look! There’s a herd of zebras! And there, in the mid­dle: a white zebra! It’s fan­tas­tic! There are white zebras! We’ll be famous!”

The sta­tis­ti­cian: “It’s not sig­nif­i­cant. We only know there’s one white zebra.”

The math­e­mati­cian: “Actu­ally, we know there exists a zebra which is white on one side.”

The com­puter sci­en­tist: “Oh no! A spe­cial case!”

The Physi­cist, the Chemist, and the Statistician

Three pro­fes­sors (a physi­cist, a chemist, and a sta­tis­ti­cian) are called in to see their dean. Just as they arrive the dean is called out of his office, leav­ing the three pro­fes­sors there. The pro­fes­sors see with alarm that there is a fire in the wastebasket.

The physi­cist says, “I know what to do! We must cool down the mate­ri­als until their tem­per­a­ture is lower than the igni­tion tem­per­a­ture and then the fire will go out.”

The chemist says, “No! No! I know what to do! We must cut off the sup­ply of oxy­gen so that the fire will go out due to lack of one of the reactants.”

While the physi­cist and chemist debate what course to take, they both are alarmed to see the sta­tis­ti­cian run­ning around the room start­ing other fires. They both scream, “What are you doing?”

To which the sta­tis­ti­cian replies, “Try­ing to get an ade­quate sam­ple size.”

One-​​Liners

Sta­tis­tics means never hav­ing to say you’re certain.

Sta­tis­tics is the art of never hav­ing to say you’re wrong.

Vari­ance is what any two sta­tis­ti­cians are at.

97.3% of all sta­tis­tics are made up.

It’s like the tale of the road­side mer­chant who was asked to explain how he could sell rab­bit sand­wiches so cheap. “Well,” he explained, “I have to put some horse-​​meat in too. But I mix them 50:50. One horse, one rab­bit.” (Dar­rel Huff, How to Lie with Sta­tis­tics)

Are sta­tis­ti­cians normal?

Smok­ing is a lead­ing cause of statistics.

43% of all sta­tis­tics are worthless.

3 out of 4 Amer­i­cans make up 75% of the population.

Death is 99 per cent fatal to lab­o­ra­tory rats.

A sta­tis­ti­cian is a per­son who draws a math­e­mat­i­cally pre­cise line from an unwar­ranted assump­tion to a fore­gone conclusion.

A sta­tis­ti­cian can have his head in an oven and his feet in ice, and he will say that on the aver­age he feels fine.

80% of all sta­tis­tics quoted to prove a point are made up on the spot.

Fett’s Law: Never repli­cate a suc­cess­ful experiment.

Clas­si­fi­ca­tion of math­e­mat­i­cal prob­lems as lin­ear and non­lin­ear is like clas­si­fi­ca­tion of the Uni­verse as bananas and non-​​bananas.

A law of con­ser­va­tion of dif­fi­cul­ties: there is no easy way to prove a deep result.

“This is a one line proof…if we start suf­fi­ciently far to the left.”

“The prob­lems for the exam will be sim­i­lar to the dis­cussed in the class. Of course, the num­bers will be dif­fer­ent. But not all of them. Pi will still be 3.14159… ”

A Greater Than Aver­age Num­ber of Legs

The great major­ity of peo­ple have more than the aver­age num­ber of legs. Amongst the 57 mil­lion peo­ple in Britain there are prob­a­bly 5,000 peo­ple who have only one leg. There­fore the aver­age num­ber of legs is

(5000 x 1 + 56,995,000 x 2)/57,000,000 = 1.9999123.

Since most peo­ple have two legs…

The Man who Counts the Num­ber of Peo­ple at Pub­lic Gatherings

You’ve prob­a­bly seen his head­lines, “Two mil­lion flock to see Pope”, “200 arrested as police find ounce of cannabis”, “Britain #3 bil­lion in debt.” You prob­a­bly won­dered who was respon­si­ble for pro­duc­ing such well rounded-​​up fig­ures. What you didn’t know was that it was all the work of one man, Rounder-​​Up to the media, John Wheeler. But how is he able to go on turn­ing out
such spot-​​on sta­tis­tics? How can he be so accu­rate all the time?

“We can’t,” admits Wheeler blithely. “Frankly, after the first mil­lion we stop count­ing, and round it up to the next mil­lion. I don’t know if you’ve ever counted a papal flock, but, not only do they look a bit the same, they also don’t keep still, what with all the bow­ing and cross­ing themselves.”

“The only way you could do it accu­rately is by tak­ing an aer­ial pho­to­graph of the crowd and hand­ing it to the com­puter to work out. But then you’d get a head­line say­ing, ‘1,678,163 [sic] flock to see Pope, not includ­ing 35,467 who couldn’t see him,’ and, believe me, nobody wants that sort of headline.”

The art of big fig­ures, avers Wheeler, lies in psy­chol­ogy, not sta­tis­tics. The pub­lic like a fig­ure it can admire. It likes mil­lion­aires, and million-​​sellers, and cen­turies at cricket, so Wheeler’s inter­na­tional agency gives them the fig­ures it wants, which involves not only round­ing up but round­ing down.

“In the old days peo­ple used to deal with crowds on the Isle of Wight principle–you know, they’d say that every day the pop­u­la­tion of the world increased by the num­ber of peo­ple who could stand upright on the Isle of Wight, or the rain-​​forests were being decreased by an area the size of Rut­land. This meant noth­ing. Most peo­ple had never been to the Isle of Wight for a start, and
even if they had, they only had a vision of lots of Chi­nese stand­ing in the grounds of the Cowes Yacht Club. And the Rut­land com­par­i­son was so use­less that they were dri­ven to abol­ish Rut­land to get rid of it.

“No, what peo­ple want is a few good mil­lions. A hun­dred mil­lion, if pos­si­ble. One of our inven­tions was street value, for instance. In the old days they used to say that police had dis­cov­ered drugs in a quan­tity large enough to get all of Rut­land stoned for a fort­night. *We* started say­ing that the drugs had a street value of #10 mil­lion. Absolutely mean­ing­less, but peo­ple under­stand it better.”

Some­times they do get the fig­ures spot on. “250,000 flock to see Royal two,” was one of his recent head­lines, and although the 250,000 was a rounded-​​up fig­ure, the two was quite cor­rect. In his pala­tial office he sits sur­rounded by relics of past headlines–a million-​​year-​​old fos­sil, a #500,000 Manet, a pho­to­graph of the Sul­tan of Brunei’s #10,000,000 house–but pride of place goes
to a pair of shoes framed on the wall.

“Why the shoes? Because they cost me #39.99. They serve as a reminder of mankind’s other great urge, to have stu­pid odd fig­ures. Strange, isn’t it? They want mass demos of exactly half a mil­lion, but they also want their gramo­phone records to go round at thirty-​​three-​​and-​​a-​​third, forty-​​five and seventy-​​eight rpm. We have stayed in busi­ness by remem­ber­ing that below a cer­tain level peo­ple want odd­ity. They don’t want a rocket cost­ing #299 mil­lion and 99p, and they don’t want a radio cost­ing exactly #50.”

How does he explain the times when the fig­ures clash–when, for exam­ple, the organ­is­ers of a demo claim 250,000 but the police put it nearer 100,000?

“We pro­vide both sets of fig­ures; the fig­ures the organ­is­ers want, and the fig­ures the police want. The pub­lic believe both. If we gave the true fig­ure, about 167,890, nobody would believe it because it doesn’t sound believable.”

John Wheeler’s name has never become well-​​known, as he is a shy fig­ure, but his firm has an annual turnover of #3 mil­lion and his eye for the right fig­ure has made him a rich man. His great­est plea­sure, how­ever, comes from the peo­ple he meets in the count­ing game.

“Exactly two bil­lion, to be precise.”

(Miles King­ton, writ­ing in The Observer, Novem­ber 3, 1986)

Final Exam

A sta­tis­tics major was com­pletely hung over the day of his final exam. It was a true/​false test, so he decided to flip a coin for the answers. The sta­tis­tics pro­fes­sor watched the stu­dent the entire two hours as he was flip­ping the coin… writ­ing the answer… flip­ping the coin… writ­ing the answer. At the end of the two hours, every­one else had left the final except for the one stu­dent. The pro­fes­sor walks up to his desk and inter­rupts the stu­dent, say­ing, “Lis­ten, I have seen that you did not study for this sta­tis­tics test, you didn’t even open the exam. If you are just
flip­ping a coin for your answer, what is tak­ing you so long?”

The stu­dent replies bit­terly (as he is still flip­ping the coin), “Shhh! I am check­ing my answers!”

The Ten Com­mand­ments of Sta­tis­ti­cal Inference

Thou shalt not hunt sta­tis­ti­cal infer­ence with a shotgun.

Thou shalt not enter the val­ley of the meth­ods of infer­ence with­out an exper­i­men­tal design.

Thou shalt not make sta­tis­ti­cal infer­ence in the absence of a model.